Model-Based Control-Loop Performance of a Continuous Direct Compaction Process
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This study is concerned with enhanced model-based control of a continuous direct compression pharmaceutical process. The control-loop performance is assessed in silico and results obtained will be incorporated into the pilot plant facility of the continuous direct compaction process at the NSF Engineering Research Center of Rutgers University. The models used in the study are obtained via system identification from a combination of first principles-based dynamic models, experimental data, and/or literature data. The main objective of the paper is to formulate an effective control strategy at the basic/regulatory level, for the integrated continuous operation of the direct compaction process, and to maintain the process at the desired set-points, taking into account the multivariable process interactions and disturbances. Simulations show that that at very mild interactions, the proposed regulatory control strategy is able to maintain set-points at desired values. However, at moderate to high process interactions, oscillatory behavior of controlled variables is seen. The presence of disturbances also resulted in poor control-loop performance. Results also lend credence to the development of advanced control strategies in such scenarios and will be addressed in future work. Optimal control tuning parameters are obtained from a derivative-free optimization algorithm.
KeywordsModel-based control Continuous processing Direct compaction Control-loop performance Pharmaceutical manufacturing
This work is supported by the National Science Foundation Engineering Research Center on Structured Organic Particulate Systems, through Grant NSF-ECC 0540855
- 8.Gernaey KV, Gani R. A model-based systems approach to pharmaceutical product-process design and analysis. Chemical Engineering Science. 2010;65:5757–69.Google Scholar
- 25.Christofides PD. Model-based control of particulate processes. Heidelberg: Springer; 2008.Google Scholar
- 27.Christofides PD. Control of nonlinear distributed parameter systems: an overview and new research directions. AICHE J. 2002;54:341–6.Google Scholar
- 30.M. Ashobi. Modeling and control of a continuous crystallization process using neural networks and model predictive control. PhD thesis, University of Saskatchewan, 1995.Google Scholar
- 36.W. Engisch, M. Ierapetritou, and F. J. Muzzio. Hopper refill of loss-in-weight feeding equipment. In Proceedings of the 2010 AIChE Annual Meeting, Salt Lake City, UT, USA, 2010.Google Scholar
- 41.Boukouvala F, Ramachandran R, Ierapetritou M, Muzzio FJ. Computational approaches for studying granular dynamics of continuous blending processes—ii. Macromol Mater Eng. 2011. doi: 10.1002/mame.201100054.
- 45.Zeng PC, Lovett D, Morris J. Process analytical technologies (PAT)—the impact for process systems engineering. Comp Aided Chem Eng. 2010;25:967–72.Google Scholar
- 46.Wu H, Heilweil EJ, Hussain AS, Khan MA. Process analytical technologies (pat)—effects of instrumental and compositional variables in terahertz spectral data quality to characterize pharmaceutical materials and tablets. Comp Aided Chem Eng. 2007;343:148–58.Google Scholar
- 50.Ogunnaike BA, Ray WH. Process dynamics, modeling and control. London: Oxford University Press; 1994.Google Scholar